{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accessing and Subsetting Variables\n",
    "\n",
    "## Objectives\n",
    "\n",
    "Introduce methods for **accessing** and **subsetting** the variables stored in ECCO `Datasets` and `DataArrays`.\n",
    "\n",
    "\n",
    "## Accessing fields inside `Dataset` and `DataArray` objects\n",
    "\n",
    "There are two methods for accessing variables stored in `DataArray` and `Dataset` objects, the \"dot\" method and the \"dictionary\" method.  The syntax of these methods is as follows:\n",
    "\n",
    "1. The \"dot\" method: e.g. ,`X.Y`\n",
    "2. The \"dictionary\" method: e.g., `Y['Y']`\n",
    "\n",
    "Both methods work identically to access *Dimensions*, *Coordinates*, and *Data variables*. Accessing *Attribute* variables requires a slightly different approach as we will see.\n",
    "\n",
    "Before we can demonstrate these methods, first create a `Dataset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import json\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import the ecco_v4_py library into Python\n",
    "## =========================================\n",
    "## -- If ecco_v4_py is not installed in your local Python library, \n",
    "##    tell Python where to find it.  For example, if your ecco_v4_py\n",
    "##    files are in /Users/ifenty/ECCOv4-py/ecco_v4_py, then use:\n",
    "\n",
    "sys.path.append('/home/ifenty/ECCOv4-py')\n",
    "import ecco_v4_py as ecco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set top-level file directory for the ECCO NetCDF files\n",
    "## =================================================================\n",
    "# base_dir = '/home/username/'\n",
    "base_dir = '/home/ifenty/ECCOv4-release'\n",
    "\n",
    "## define a high-level directory for ECCO fields\n",
    "ECCO_dir = base_dir + '/Release3_alt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading files of  SSH\n",
      "loading files of  OBP\n"
     ]
    }
   ],
   "source": [
    "## Load the model grid\n",
    "grid_dir= ECCO_dir + '/nctiles_grid/'\n",
    "\n",
    "ecco_grid = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc')\n",
    "\n",
    "## Load one year of SSH and OBP\n",
    "day_mean_dir= ECCO_dir + '/nctiles_monthly/'\n",
    "\n",
    "ecco_vars = \\\n",
    "    ecco.recursive_load_ecco_var_from_years_nc(day_mean_dir, \\\n",
    "                                               vars_to_load=['SSH','OBP'], \\\n",
    "                                               years_to_load=2010,\\\n",
    "                                               dask_chunk=False)\n",
    "\n",
    "## Merge the ecco_grid with the ecco_vars to make the ecco_ds\n",
    "ecco_ds = xr.merge((ecco_grid , ecco_vars))                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    SSH      (time, tile, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    OBP      (time, tile, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecco_ds.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int64 0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50\n",
       "  * tile       (tile) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "    XG         (tile, j_g, i_g) float32 -115.0 -115.0 ... -102.928925 -108.95171\n",
       "    YG         (tile, j_g, i_g) float32 -88.17569 -88.31587 ... -88.02409\n",
       "    CS         (tile, j, i) float32 0.06157813 0.06675376 ... -0.9983638\n",
       "    SN         (tile, j, i) float32 -0.99810225 -0.9977695 ... -0.057182025\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    Zp1        (k_p1) float32 0.0 -10.0 -20.0 -30.0 ... -5244.5 -5678.0 -6134.5\n",
       "    Zu         (k_u) float32 -10.0 -20.0 -30.0 -40.0 ... -5244.5 -5678.0 -6134.5\n",
       "    Zl         (k_l) float32 0.0 -10.0 -20.0 -30.0 ... -4834.0 -5244.5 -5678.0\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "    dxG        (tile, j_g, i) float32 15584.907 15589.316 ... 23142.107\n",
       "    dyG        (tile, j, i_g) float32 23210.262 23273.26 ... 15595.26 15583.685\n",
       "    Depth      (tile, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    rAz        (tile, j_g, i_g) float32 179944260.0 180486990.0 ... 364150620.0\n",
       "    dxC        (tile, j, i_g) float32 15583.418 15588.104 ... 23406.256\n",
       "    dyC        (tile, j_g, i) float32 11563.718 11593.785 ... 15578.138\n",
       "    rAw        (tile, j, i_g) float32 361699460.0 362790240.0 ... 364760350.0\n",
       "    rAs        (tile, j_g, i) float32 179944260.0 180486990.0 ... 364150620.0\n",
       "    drC        (k_p1) float32 5.0 10.0 10.0 10.0 ... 399.0 422.0 445.0 228.25\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    PHrefF     (k_p1) float32 0.0 98.1 196.2 ... 51448.547 55701.18 60179.445\n",
       "    hFacC      (k, tile, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacW      (k, tile, j, i_g) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacS      (k, tile, j_g, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    maskC      (k, tile, j, i) bool False False False ... False False False\n",
       "    maskW      (k, tile, j, i_g) bool False False False ... False False False\n",
       "    maskS      (k, tile, j_g, i) bool False False False ... False False False\n",
       "    maskCtrlW  (k, tile, j, i_g) bool False False False ... False False False\n",
       "    maskCtrlS  (k, tile, j_g, i) bool False False False ... False False False\n",
       "    maskCtrlC  (k, tile, j, i) bool False False False ... False False False\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecco_ds.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing Data Variables\n",
    "\n",
    "Now we'll use the two methods to access the ``SSH`` `DataArray`,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## The Dot Method\n",
    "ssh_A = ecco_ds.SSH\n",
    "\n",
    "## The Dictionary Method\n",
    "ssh_B = ecco_ds['SSH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'xarray.core.dataarray.DataArray'>\n",
      "<class 'xarray.core.dataarray.DataArray'>\n"
     ]
    }
   ],
   "source": [
    "print (type(ssh_A))\n",
    "print (type(ssh_B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We access the `numpy` arrays stored in these `DataArrays` with the \"dot\" method on `values`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "ssh_arr = ssh_A.values\n",
    "print (type(ssh_arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The ``numpy`` array storing the data\n",
    "\n",
    "The shape of the `numpy` array can be found by invoking its `.shape`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 13, 90, 90)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The order of these four dimensions is consistent with their ordering in the original `DataArray`,\n",
    "~~~\n",
    "time, tile, j, i\n",
    "~~~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('time', 'tile', 'j', 'i')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_A.dims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``ssh_A`` and ``ssh_B`` are new variables but they are not **copies** of the original  ``SSH`` `DataArray` object, they both point to the original `numpy` array.  \n",
    "\n",
    "We can confirm that ``ssh_A`` and ``ssh_B`` both refer to the same array in memory using the Python `allclose` command (and ignoring nans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(np.allclose(ssh_A, ssh_B, equal_nan=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing the numpy array\n",
    "\n",
    "We have *ssh_arr*, a 4D numpy array (``ndarray``).  Let's take out a single 2D slice of it, the first time record, and the second tile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f71ae07cac8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "\n",
    "# plot SSH for time =0, tile = 2 by using ``imshow`` on the ``numpy array``\n",
    "plt.imshow(ssh_arr[0,2,:,:], origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could accessing the array using the \"dot\" method on *ecco_ds* to first get *SSH* and then use the \"dot\" method to access the ``numpy`` array through *values*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f71adfa7438>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "plt.imshow(ecco_ds.SSH.values[0,2,:,:], origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or could use the \"dictionary\" method on *ecco_ds* to get *SSH* then the \"dot\" method to access the ``numpy`` array through *values*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f71adf1e2e8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "plt.imshow(ecco_ds['SSH'].values[0,2,:,:], origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are equivalent methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing Coordinates\n",
    "\n",
    "Accessing Coordinates is exactly the same as accessing *Data variables*.  Use the \"dot\" or \"dictionary\" methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'xarray.core.dataarray.DataArray'>\n",
      "<class 'xarray.core.dataarray.DataArray'>\n"
     ]
    }
   ],
   "source": [
    "xc = ecco_ds['XC']\n",
    "time = ecco_ds['time']\n",
    "\n",
    "print(type(xc))\n",
    "print(type(time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As *xc* is a ``DataArray``, we can access the values in its ``numpy`` array through ``.values``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-111.60647 , -111.303   , -110.94285 , ...,   64.791115,\n",
       "           64.80521 ,   64.81917 ],\n",
       "        [-104.8196  , -103.928444, -102.87706 , ...,   64.36745 ,\n",
       "           64.41012 ,   64.4524  ],\n",
       "        [ -98.198784,  -96.788055,  -95.14185 , ...,   63.936497,\n",
       "           64.008224,   64.0793  ],\n",
       "        ...,\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ]],\n",
       "\n",
       "       [[ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        ...,\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ]],\n",
       "\n",
       "       [[ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,\n",
       "           50.5     ,   51.5     ],\n",
       "        ...,\n",
       "        [ -37.730072,  -37.17829 ,  -36.597565, ...,   50.597565,\n",
       "           51.17829 ,   51.730072],\n",
       "        [ -37.771988,  -37.291943,  -36.764027, ...,   50.764027,\n",
       "           51.291943,   51.771988],\n",
       "        [ -37.837925,  -37.44421 ,  -36.968143, ...,   50.968143,\n",
       "           51.44421 ,   51.837925]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-127.83792 , -127.77199 , -127.73007 , ..., -127.5     ,\n",
       "         -127.5     , -127.5     ],\n",
       "        [-127.44421 , -127.29195 , -127.17829 , ..., -126.5     ,\n",
       "         -126.5     , -126.5     ],\n",
       "        [-126.96814 , -126.76402 , -126.597565, ..., -125.5     ,\n",
       "         -125.5     , -125.5     ],\n",
       "        ...,\n",
       "        [ -39.031857,  -39.235973,  -39.402435, ...,  -40.5     ,\n",
       "          -40.5     ,  -40.5     ],\n",
       "        [ -38.55579 ,  -38.708057,  -38.82171 , ...,  -39.5     ,\n",
       "          -39.5     ,  -39.5     ],\n",
       "        [ -38.162075,  -38.228012,  -38.269928, ...,  -38.5     ,\n",
       "          -38.5     ,  -38.5     ]],\n",
       "\n",
       "       [[-127.5     , -127.5     , -127.5     , ..., -127.5     ,\n",
       "         -127.5     , -127.5     ],\n",
       "        [-126.5     , -126.5     , -126.5     , ..., -126.5     ,\n",
       "         -126.5     , -126.5     ],\n",
       "        [-125.5     , -125.5     , -125.5     , ..., -125.5     ,\n",
       "         -125.5     , -125.5     ],\n",
       "        ...,\n",
       "        [ -40.5     ,  -40.5     ,  -40.5     , ...,  -40.5     ,\n",
       "          -40.5     ,  -40.5     ],\n",
       "        [ -39.5     ,  -39.5     ,  -39.5     , ...,  -39.5     ,\n",
       "          -39.5     ,  -39.5     ],\n",
       "        [ -38.5     ,  -38.5     ,  -38.5     , ...,  -38.5     ,\n",
       "          -38.5     ,  -38.5     ]],\n",
       "\n",
       "       [[-127.5     , -127.5     , -127.5     , ..., -115.850204,\n",
       "         -115.50567 , -115.166985],\n",
       "        [-126.5     , -126.5     , -126.5     , ..., -115.78025 ,\n",
       "         -115.464066, -115.153244],\n",
       "        [-125.5     , -125.5     , -125.5     , ..., -115.71079 ,\n",
       "         -115.42275 , -115.139595],\n",
       "        ...,\n",
       "        [ -40.5     ,  -40.5     ,  -40.5     , ..., -101.42989 ,\n",
       "         -106.83081 , -112.28605 ],\n",
       "        [ -39.5     ,  -39.5     ,  -39.5     , ..., -100.48844 ,\n",
       "         -106.24874 , -112.090065],\n",
       "        [ -38.5     ,  -38.5     ,  -38.5     , ...,  -99.42048 ,\n",
       "         -105.58465 , -111.86579 ]]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xc.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the *xc* is 13 x 90 x 90.  Unlike *SSH* there is no time dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 90, 90)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xc.values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing Attributes\n",
    "\n",
    "To access *Attribute* fields you you can use the dot method directly on the ``Dataset`` or ``DataArray`` or the dictionary method on the ``attrs`` field. \n",
    "\n",
    "To demonstrate: we access the ``units`` attribute on *OBP* using both methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'m'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecco_ds.OBP.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'m'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecco_ds.OBP.attrs['units']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting variables using the``[]``, ``sel``,  ``isel``, and ``where`` syntaxes\n",
    "\n",
    "So far, a considerable amount of attention has been placed on the *Coordinates* of `Dataset` and `DataArray` objects.  Why?  Labeled coordinates are certainly not necessary for calculations on the basic numerical arrays that store the ECCO state estimate fields.  The reason so much attention has been placed on coordinates is because the `xarray` offers several very useful methods for selecting (or indexing) subsets of data.\n",
    "\n",
    "For more details of these *indexing* methods, please see the excellent ``xarray`` documentation:\n",
    "http://xarray.pydata.org/en/stable/indexing.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting `numpy` arrays using the **[ ]** syntax\n",
    "\n",
    "Subsetting `numpy` arrays is simple with the standard Python **[ ]** syntax.  \n",
    "\n",
    "To demonstrate, we'll pull out the ``numpy`` array of *SSH* and then select the first time record and second tile\n",
    "\n",
    "  > **Note:**  `numpy` array indexing starts with 0*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 13, 90, 90)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_arr = ecco_ds.SSH.values\n",
    "type(ssh_arr)\n",
    "ssh_arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know the order of the dimensions of SSH is [time, tile, j, i], so :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssh_time_0_tile_2 = ssh_arr[0,2,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*ssh_time_0_tile_2* is also a numpy array, but now it is a 2D array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 90)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_time_0_tile_2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We know that the *time* coordinate on *ecco_ds* has one dimension, **time**, so we can use the ``[]`` syntax to find the first element of that array as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('time',)\n",
      "2010-01-16T12:00:00.000000000\n"
     ]
    }
   ],
   "source": [
    "print(ecco_ds.time.dims)\n",
    "\n",
    "# the first time record is \n",
    "print(ecco_ds.time.values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which confirms that the first time record corresponds with Jan 2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a 2D array in *ssh_time_0_tile_2*, let's plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "plt.imshow(ssh_time_0_tile_2, origin='lower',cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('<SSH> of Tile 2 for Jan 2010 [m]');\n",
    "plt.xlabel('x-index');\n",
    "plt.ylabel('y-index');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By eye we see large negative values around x=0, y=70 (i=0, j=70).  Let's confirm: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.88685966"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remember the order of the array is [tile, j, i]\n",
    "ssh_time_0_tile_2[70,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot *SSH* in this tile from y=0 to y=89 along x=0 (from the subtropical gyre into the subpolar gyre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oEfd/soSr+7fi1iHtSWxYtYGDxhhjjrBEX4G4yHDm/Gk47302jYiUjszduI95G/fzzqxNTne7qvsbmsQ0oENSDBf3SSU9KZaOybF0bR5HdIRV8fFIjAzhosx23Dy4LYu3ZDPu+/W8+u063pm1iRsHteW6AW08GXNgjDF1lf3FrISI0Cw6hMzeqYzsneq3jKraveLVTETo3iKef13ak5sGteVfX67gsckrGffden57Zkcu65dmdW6MMVVg9zZVA0s4NSs9KZYXrs7go1tOpV2zGO79cBF/+GgxBUXHzuRnjDHmaJboTZ3Ru2Ujxt/Qn1sy2/H2jxu56uWZ7Mk9XPkHjTGmHrNEb+qUkBDhd2d14onLejF/035GPPMty9z5A4wxxhzLEr2pk0b2TuW9m06hoKiEi5/7jh/X7fU6JGOMqZUs0Zs6q2daAp/cPoDkuEh+9cacn+bzN8YYc4QlelOnJcVF8uLoDAqKSrjx9dnkFRR7HZIxxtQqluhNndeuaQxPXtGLpduyueeDhaiq1yEZY0ytYYneBIWhnZL4zRkdmbBgKy/MWOt1OMYYU2tYojdB45bMdpzXI4V/fL6crBU7vQ7HGGNqBUv0JmiICI9e0oNOyXHcOX4+O3PyvQ7JGGM8Z4neBJXoiDCe+UVv8guL+fP/Ftv1emNMvWeJ3gSdtk1juPv0DnyxZAeTFm33OhxjjPGUp4leRM4SkRUislpE7vWzvoGIvOOunykirQMfpamLrhvQhh4t4vnLx4vZe7DA63CMMcYzniV6EQkFngHOBroAV4hIlzLFrgP2qWp74HHgH4GN0tRVYaEhPHpJD7LzC3ngkyVeh2OMMZ7x8jG1JwGrVXUtgIiMB0YAS33KjADud1+/D/xHRETtwqupgk7Jcdw2JJ3Hv1rJeT2ac3qXJK9DMsYE2KvfruOlr9d5HcYxHr6oO4M6NA3IvrxM9KnAJp/lzcDJ5ZVR1SIROQA0Bnb7FhKRG4EbAZKSksjKyqrWQHNzc6t9m8a/6q7rriFKWmwIv31nDg8NiKJhuD1SuJSd14FjdR1YvvX90ex8sg8W06Opl+nuWGuXLaRka2hA9uXlkfv7i1u2pV6VMqjqWGAsQEZGhmZmZv7s4HxlZWVR3ds0/tVEXSd1OMDIZ79lyr5GPDaqV7Vuuy6z8zpwrK4Dy7e+n172Hd3jQ3jzhv7eBuUhLwfjbQbSfJZbAFvLKyMiYUA8YI8pM8ele4t4bs1sx4dzt/D54m1eh2OMCaCc/EJiI2tXaz7QvEz0s4B0EWkjIhHA5cCEMmUmAKPd15cAU+36vDkRtw9Lp3tqPL//cJFNpGNMPZKTX0RsZLjXYXjKs0SvqkXAbcAXwDLgXVVdIiJ/FZEL3GIvA41FZDVwN3DMLXjGVEV4aAiPX9aTQwXF/P6DRTaRjjH1hJPo63eL3tOjV9VJwKQy7/3F53U+cGmg4zLBqX2zWO45qxN/nbiUd2Zt4vKTWnodkjGmBhWXKLmHrUVvM+OZemXMqa05tV1j/jZxKRv3HPI6HGNMDco9XARAXD1v0VuiN/VKSIjwz0t7EiLC3e/Op6CoxOuQjDE1JCe/EKDed91bojf1TmpCFA9d1J3ZG/bxf+/Mp7jErtcbE4xy8p0WfX3vuq/fX3NMvXVBz+bsOJDPQ5OWERsZxsMXdUfEJtMxJpgcSfT1O9XV76M39doNg9pyIK+Q/0xbTVxUOL8/u5Mle2OCyJGue2vRG1Nv/fqMDmTnFzJ2xlrio8K5dUh7r0MyxlQTa9E76vfRm3pPRLj//K5k5xXyzy9WUFBUwm1D2xMeasNXjKnrbDCeo34fvTEcGYkP8OSUVXy5dAePXtyD7i3iPY7MGPNzZOeX3l5Xv7vurdliDM7MeU9c3pvnr+rLntzDjHjmGx6etIy8gmKvQzPGnKCc/CIiQkOIDA/MU+JqK2vRG+PjrG7JnNKuMQ9PWsYLM9by2eLtjDm1NRf2TqVRwwivwzPGHAd7oI3DWvTGlBEfFc4jF/fgretPplF0OH+duJST/z6F29+ex7erd1Ni990bUyfYPPcOqwFjynFq+yZ8fNsAlm3L5p1Zm/ho3hY+WbCV5vGRnNM9hXN7pNArLcFuyTOmlnJa9PX7+jxYojemUp1T4rj/gq7ce3YnvliynU8WbGXc9+t56Zt1pCZEcW6PFEZlpNG+WYzXoRpjfFiL3mE1YEwVRYaHMqJXKiN6pXIgr5Cvlu7g00XbePXbdYydsZb+bRO5un9rzuiaZLfnGVMLZOcX0qZJQ6/D8JwlemNOQHxUOBf3bcHFfVuwK+cw787exFszN3LrW3NpGtuAq/u34toBbYhpYP/FjPGK06K3rntrdhjzMzWNbcCtQ9oz43dDeHVMP7qnxvPY5JUMfnQa475bb0/IM8Yj1nXvsERvTDUJDRGGdGrGK2P68b9bT6N9sxjum7CE4Y9NZ8KCrajaaH1jAqW4RMk9bC16sERvTI3olZbA+Bv78+ov+xEdEcodb8/j6pd/ZOv+PK9DM6ZeyD1cOiuetegt0RtTQ0SEIR2b8ekdA3lwZDfmbtzHmY/P4P05m611b0wNs3nuj7BEb0wNCw0Rrurfis/vHETnlDh+894CbvzvHHblHPY6NGOC1pEn11nXvSV6YwKkZeNo3r6xP386tzPTV+7i7Cdn8OO6vV6HZUxQskfUHmGJ3pgACg0Rrh/Ylom3DyAuMpwrX/qBt2Zu9DosY4LOka57a9FbojfGAx2SYvno1tM4tV0T/vDRIv70v0V2G54x1cha9Ed4kuhFJFFEJovIKvd3Iz9leonI9yKyREQWishlXsRqTE2JjwrnlTH9uGlwW974YSNXvTyTPbl23d6Y6mCD8Y7wqkV/LzBFVdOBKe5yWYeAa1S1K3AW8ISIJAQwRmNqXGiI8PuzO/Pk5b1YsGk/Fz/3HRv3HPI6LGPqvOz80tvrrOveq0Q/Ahjnvh4HjCxbQFVXquoq9/VWYCfQNGARGhNAI3ql8tYN/dmfV8hFz33H4i0HvA7JmDotJ7+I8FChQZhdoRYv7ucVkf2qmuCzvE9Vj+m+91l/Es4Xgq6qesyFTBG5EbgRICkpqe/48eOrNd7c3FxiYuzJZIFQ3+t6a24J/56dz8FC5fbekXRtElpj+6rvdR1IVteBlZubywcbwpm9vYinh9WPh9oMGTJkjqpm+FtXYxcvROQrINnPqj8e53ZSgP8Co/0leQBVHQuMBcjIyNDMzMzjC7YSWVlZVPc2jX9W1zB0YD5jXv2RJ+bl8q9LezKiV2qN7MfqOnCsrgMrKyuL2MR4Eg/tt3qnBhO9qg4vb52I7BCRFFXd5ibyneWUiwM+Bf6kqj/UUKjG1CrJ8ZG8c9Mp3PD6bO4cP58d2fncMLAtIuJ1aMbUGTn5hTYQz+XVxYsJwGj39Wjg47IFRCQC+Ah4XVXfC2BsxnguPiqc1689iXN7pPD3Scu5b8ISikts2lxjqionv4jYBjYQD7xL9I8Ap4vIKuB0dxkRyRCRl9wyo4BBwBgRme/+9PImXGMCLzI8lKcv781Ng9ry+vcbuOm/szlUUOR1WMbUCfaI2iM8qQVV3QMM8/P+bOB69/UbwBsBDs2YWiUkRPj9OZ1JbRTF/ROWcMXYH3hpdD+axjbwOjRjajWn695a9GAz4xlTJ1xzSmteuDqDFTtyOPepr/luzW6vQzKmVrMW/RGW6I2pI07vksSHvzqNmMgwrnxpJv/6YgVFxTZtrjFllaiSW1Bkz6J3WaI3pg7p0jyOibcP4NK+LfjPtNWMeuF7Nu21mfSM8ZVfBKr2QJtSluiNqWOiI8J49JKePHVFb1btyOWcp75m7Iw15BcWex2aMbVCXpFzh4p13Tss0RtTR13QszmT7hxIn5aN+Puk5Qz793Q+mLPZbsMz9d4h9+YUa9E7LNEbU4elJUYz7tqTeOv6k0lsGMGv31vAuU99zeeLt9v1e1NvWYv+aBXWgogsrMI2dqnqMbfKGWMC59T2Tfj41tOYuGgb//piBTe/MYfkuEhG9Uvj8n5pNE+I8jpEYwLmUKElel+V1UIocE4F6wVnljtjjMdCQoQLejbn7G7JTF2+k7dmbuTpqav4z9RVDOnYjEszWjCkUzMahNXcg3KMqQ3yrOv+KJUl+ptUdUNFBUTklmqMxxjzM4WHhnBm12TO7JrMpr2HeGfWJt6dvYkpy3eSEB3O+T2ac3HfFnjx5EpjAqG0695ur3NUWAuq+k1lG6hKGWOMN9ISo/nNmR25a3g636zezYdzt/Du7E3894cNpMYId8VuYkSvVCLsmd0miBz66Rq9teihilPgish5wN+AVu5nBFBVjavB2Iwx1SQsNITMjs3I7NiM7PxCJi3cxjOTl/Db9xfy7y9Xct2ANlx+Upr9YTRBIa8QwkKEyHD7AgtVn+v+CeAiYJFaf58xdVpcZDiXn9SSpINrkOZdeWH6Wh6atIynpq7i9qHtufa0NoSF2h9IU3flFSlxUeH2aGdXVf83bwIWW5I3JniICJkdm/H2jf35+NbTyGjl3I9/wX++Zf6m/V6HZ8wJO1SkNuLeR1Vr4nfAJBGZDhwufVNVH6uRqIwxAdUzLYFXxvTjiyXbuW/CEi589ltGn9KaX5/RwbrzTZ2TV2S31vmqaov+IeAQEAnE+vwYY4KEiHBWtxS+unsw1/Rvxbjv13Pm4zNYvj3b69CMOS55RUpsA/uCWqqqX3kSVfWMGo3EGFMrxEaG88CIbozsncrNb8zh0ue+54Wr+3Jq+yZeh2ZMlRwqVFpbi/4nVW3RfyUiluiNqUd6t2zER7ecRkpCJKNf/ZGP52/xOiRjqsTpurcWfamqJvpbgc9FJE9EskUkR0SsP8+YINc8IYr3bj6Vvq0acef4+TyXtcYm2jG1ng3GO1qVEr2qxqpqiKpGqWqcu2z30BtTD8RHhTPu2pM4v2dz/vH5ch7/apXXIRlTrpISJb/IZsXzVdlDbZJVdfvPLWOMqdsahIXy5GW9aBAWwlNTVtEjNZ7hXZK8DsuYYxwsKEKxrntflbXoJ1VhG1UpY4yp40JChAdHdqNbahx3vzufjXsOeR2SMcfIyXeeaGNd90dUluh7utfky/vJAexrvTH1RGR4KM9d2ReAX705h/zCYo8jMuZo2fmFgLXofVWY6FU11L0mX95PrKqmBipYY4z30hKjeeLyXizZms19Hy/xOhxjjmIt+mPZhNbGmOM2tFMStw9tzzuzN/HOrI1eh2PMT3J+atFboi/lSaIXkUQRmSwiq9zfjSooGyciW0TkP4GM0RhTsbuGd2BA+yb8+eMlrN990OtwjAF8W/TWdV/Kqxb9vcAUVU0HprjL5fkbMD0gURljqiw0RHhsVE/CQoRHv1judTjGAJDtJnq7ve6IChO92/Iu9+dn7HcEMM59PQ4YWc7+++IM9vvyZ+zLGFNDmsVFcuOgtkxatJ05G/Z5HY4xPl331qIvJRXNciUi6wAFBEgBtrqvAVRV257QTkX2q2qCz/I+VW1UpkwIMBW4GhgGZKjqbeVs70bgRoCkpKS+48ePP5GwypWbm0tMTEy1btP4Z3UdONVV14eLlHu+zqNJlPDHkyPtGeB+2HkdOO+tKOCz9QW8fEbDenUuDhkyZI6qZvhbV2Hfhqq2KX0tIvNUtXdVdyoiXwHJflb9sYqbuAWYpKqbKvvHUtWxwFiAjIwMzczMrGqYVZKVlUV1b9P4Z3UdONVZ1/vjN3LPB4vIa9KJc7qnVMs2g4md14Hz1f5FRG/eyJAhQ7wOpdY4nosYxzXBtaoOL2+diOwQkRRV3SYiKcBOP8VOAQaKyC1ADBAhIrmqWtH1fGOMBy7pm8Yr36znkc+WM7xzEhFhdkOP8UZOfhFRYfWnJV8VXv1vnACMdl+PBj4uW0BVr1TVlqraGvgN8LoleWNqp9AQ4Q/ndmbj3kP894cNXodj6rGc/CKiwy3R+6psrvu7fRablVlGVR87wf0+ArwrItcBG4FL3f1lADer6vUnuF1jjEcGd2jKwPQmPD11FZf0aUF8tA2GMoGXk19IlA24P0plLfpYn58XyyzHnuhOVXWPqg5T1XT39173/dn+kryqvlbeQDxjTO3xh3M6cyCvkGezVl8a634AABr+SURBVHsdiqmnrOv+WJUNxnsgUIEYY+q+zilxjOyVyn9/2MAtme2tVW8CLie/iMbRluh9VXYf/Q0iku6+FhF5RUQOiMhCEanyCHxjTP1x0+C2HCoo5o2Zdq3eBF62dd0fo7Ku+zuB9e7rK4CeQFvgbuCpmgvLGFNXdUqOI7NjU179dp093c4EVEmJknu4iCgbjHeUyhJ9kaoWuq/Pwxn5vkdVvwIa1mxoxpi66qZB7didW8CHc7d4HYqpRw4WFKEK0XaN/iiVJfoSEUkRkUic2em+8lkXVXNhGWPqsv5tE+nZIp6xM9ZQXHJcU3AYc8JKH2hjXfdHqyzR/wWYjdN9P0FVlwCIyGBgbc2GZoypq0SEmwa3Y/2eQ3y5ZLvX4Zh6ojTRW4v+aBUmelWdCLQCOqvqDT6rZgGX1WRgxpi67cyuybRuHM3z09dQ0TM1jKku63bnAhATYYneV2Wj7vsBTVR1n7t8jYh8jDPhTUQA4jPG1FGhIcINg9qyYPMBZq7b63U4JsipKs9mrSEtMYoOjWwKZl+V1cYLQAGAiAzCSfCvAwdwHyJjjDHlubhPC5rERPDC9DVeh2KC3JRlO1m4+QC3D0knLMRa9L4qS/ShpbPW4XTVj1XVD1T1z0D7mg3NGFPXRYaHMubU1kxbsYvl27O9DscEKVXlsckradU4mgv7pHodTq1TaaIXkdLxi8Nwng9fysY1GmMqdVX/VkSFh/LS1+u8DsUEqS+W7GDptmzuGJpOeKh125dVWY28DUx3r8vnAV8DiEh7nO57Y4ypUEJ0BKMyWvDx/C3szM73OhwTZEpKlCe+WknbJg0Z0au51+HUSpWNun8I+DXwGjBAjwydDQFur9nQjDHB4toBbSgqUcZ9v97rUEyQ+WzxdpZvz+HO4emEWWver0prRVV/UNWPVPWgz3srVXVuzYZmjAkWrRo35Mwuybzxw0YOFRR5HY4JEsVua759sxjO62Gt+fLY1x9jTEDcMKgNB/IKeX/OZq9DMUHi00XbWLUzl7uGpxNqI+3LZYneGBMQfVsl0rtlAi9/s86mxTU/2+Z9h3jo06V0TIrlnG4pXodTq1miN8YEzA0D27JhzyEmL93hdSimDtude5irX/6RvIJinri8FyHWmq+QJXpjTMCc2TWZtMQoXvzaHpVhTkxOfiGjX/mRbQfyeGVMPzqnxHkdUq1nid4YEzChIcK1p7VhzoZ9zN24z+twTB2TX1jM9eNms2J7Ds9d1ZeM1oleh1QnWKI3xgTUqIw04iLDeHGGtepN1RUUlXD72/P4cf1e/j2qJ0M6NvM6pDrDEr0xJqAaNgjj6lNa8fmS7azemeN1OKaWKyou4b3Zmxj2WBaTl+7ggQu6MqKXTXN7PCzRG2MC7roBbYkKD+U/U1d7HYqppYpLlI/nb+GMx2fw2/cXkhAVwWu/7Mc1p7T2OrQ6x+arN8YEXGLDCK7u34oXv17LncM70KZJQ69DMrXIvoMFjHn1RxZsPkCn5FjGXt2X07skIWKj60+EJy16EUkUkckissr93aicci1F5EsRWSYiS0WkdWAjNcbUlOsHtiUiLIRnplmr3hyx/1ABV740k+Xbc3jisl5MumMgZ3RNtiT/M3jVdX8vMEVV04Ep7rI/rwP/VNXOwEnAzgDFZ4ypYU1jG3DFSS35aN4WNu095HU4phY4cKiQK1+ayepdubx4TQYje6faPfLVwKtEPwIY574eB4wsW0BEugBhqjoZQFVzVdX+GhgTRG4a1I5QEZ7NslZ9fXcgr5CrXp7Jqh25jL26L4M6NPU6pKDhVaJPUtVtAO5vf/dJdAD2i8iHIjJPRP4pIqEBjdIYU6OS4yO5rF8a78/ZzJb9eV6HYzySnV/INS/PZPn2bJ6/ug+ZdutctZIjT56t5g2LfAUk+1n1R2Ccqib4lN2nqkddpxeRS4CXgd7ARuAdYJKqvuxnXzcCNwIkJSX1HT9+fLUdB0Bubi4xMTHVuk3jn9V14NSWut6TV8LvZuQxOC2Ma7o08DqcGlFb6rq2emPpYaZtKuK23g3o3eznjxGvj/U9ZMiQOaqa4W9djY26V9Xh5a0TkR0ikqKq20QkBf/X3jcD81R1rfuZ/wH9cZJ/2X2NBcYCZGRkaGZmZjUcwRFZWVlU9zaNf1bXgVOb6nrWoYV8OHcLf7+yP8nxkV6HU+1qU13XNvsPFfCrKVMZ2bsF/zeqZ7Vs0+r7aF513U8ARruvRwMf+ykzC2gkIqUXaoYCSwMQmzEmwG7JbI+iPDZ5hdehmAB7c+ZG8gqLuX5gG69DCVpeJfpHgNNFZBVwuruMiGSIyEsAqloM/AaYIiKLAAFe9CheY0wNatk4mjGntua9OZtZvOWA1+GYADlcVMxr361nYHoTezhNDfIk0avqHlUdpqrp7u+97vuzVfV6n3KTVbWHqnZX1TGqWuBFvMaYmnf7sHQSoyN44JMl1NTYIVO7TJi/lV05h7lhYFuvQwlqNgWuMaZWiIsM5zdndmTW+n1MXLjN63BMDVNVXvp6HZ2SYxmY3sTrcIKaJXpjTK0xKiONzilxPPLZcvILi70Ox9SgGat2s2JHDtcPbGuz3tUwS/TGmFojNES47/wubNmfx1h7jG1Qe3HGWprFNuCCns29DiXoWaI3xtQq/ds25pzuyTyXtYZtB2wSnWC0dGs236zezZjTWhMRZmmoplkNG2Nqnd+f3ZliVR6etNzrUEwNeOnrtURHhHLlSa28DqVesERvjKl10hKj+dXgdkxYsJVPbWBeUNl3sIBPFm5lVEYa8dHhXodTL1iiN8bUSrcNbU+vtATu/XChPd0uiHy+ZDuFxcolfVt4HUq9YYneGFMrhYeG8PQVvUHhjvHzKCwu8TokUw0mLtxK68bRdG1uE+QEiiV6Y0ytlZYYzd8v6s68jft5fPJKr8MxP9OunMN8v2YP5/VobrfUBZAlemNMrXZ+z+ZclpHGc9PX8O3q3V6HY36Gz5dsp0ThvJ4pXodSr1iiN8bUevdd0IW2TRpy1zvz2Z172OtwzAmauGAr7ZvF0DEp1utQ6hVL9MaYWi86Ioynr+jDgbxCrnttFtn5hV6HZI7Tjux8fly/l3O7p1i3fYBZojfG1Aldmsfx7C/6sGRrNte+OotDBUVeh2SOw6RF21CF863bPuAs0Rtj6ozhXZJ48vLezN24j+vHzbb58OuQTxduo1NyLO2bWbd9oFmiN8bUKef2SOHfo3ry/do93PzGHA4XWbKv7bbuz2P2hn2c291a816wRG+MqXMu7N2Chy/sTtaKXdz21jzyCizZ12aTFjmzG55nD7DxhCV6Y0yddPlJLfnriK58tWwHFz77Let2H/Q6JFOOiQu30bV5HG2aNPQ6lHrJEr0xps665pTWvPbLk9ienc8FT3/DF0u2ex2SKWPT3kPM37Sfc3tYt71XLNEbY+q0wR2aMvH2AbRt2pCb/juHhz9bRpFNl1trTHQfSnRed+u294olemNMndeiUTTv3nwKV/VvyQvT1zLimW/5cd1er8Oq94qKS3jjhw2c1DqRlo2jvQ6n3rJEb4wJCg3CQnlwZHeevbIPew8WMOqF77n1zbn25DsPfbpoG1v253HDoLZeh1KvhXkdgDHGVKdzuqcwpGMzXpixhuenr2Hysh3cMLAN157WhsYxDbwOr95QVV78ei1tmzZkWKdmXodTr1mL3hgTdKIiQrlreAem/jqTs7sl88y0NZzyyFR+9/4Clm3L9jq8euH7NXtYvCWbGwa2JSTEprz1krXojTFBq3lCFE9e3pvbhrTn1e/W8+Hczbw7ezMnt0nkyv6tGN65GdER9mewJrwwYy1NYhpwYe9Ur0Op9zw5w0UkEXgHaA2sB0ap6j4/5R4FzsXpeZgM3KmqGrhIjTHBID0plr9f2J3fndmRd2dvYtx3G7jj7XlEhocwrFMS5/VIIbNjM6IiQr0ONSgs357N9JW7+M0ZHYgMtzr1mldfZe8FpqjqIyJyr7t8j28BETkVOA3o4b71DTAYyApgnMaYIJIQHcGNg9px3YC2zFq/l4kLt/LZou18umgb0RGhDGjfhKGdmjGkUzOS4iK9DrfOGjtjLdERoVzVv5XXoRi8S/QjgEz39Tic5H1PmTIKRAIRgADhwI7AhGeMCWahIUL/to3p37Yx95/flZnr9vLpom1MW76TL5c6f2a6No9jWKdmDO+SRLfm8XaduYq2HchjwvytXNW/FQnREV6HYwDxoidcRParaoLP8j5VbeSn3L+A63ES/X9U9Y/lbO9G4EaApKSkvuPHj6/WeHNzc4mJianWbRr/rK4Dx+r6WKrK5lxlwa4iFu4qZtW+EhRIaCD0ahZK72ahdG0cSthxJv36VNfvrCjg83WFPDooiqbR3oz3rk/1XWrIkCFzVDXD37oaa9GLyFdAsp9VfpO1n8+3BzoDLdy3JovIIFWdUbasqo4FxgJkZGRoZmbmCcVcnqysLKp7m8Y/q+vAsbou39Xu770HC5i2fCdfLdvB9JW7yNp0mEbR4YzolcrFfVrQLTUOkcqTfn2p670HC7h92jTO7ZHCpef08SyO+lLfVVVjiV5Vh5e3TkR2iEiKqm4TkRRgp59iFwI/qGqu+5nPgP7AMYneGGNqQmLDCC7u24KL+7Ygv7CYb1fv5sN5W3jrx4289t16OiTFMCojjVH90oiLDPc6XE8Vlyh3jp/H4aISbh+a7nU4xodX99FPAEa7r0cDH/spsxEYLCJhIhKOMxBvWYDiM8aYo0SGhzKscxLP/KIPs/4wnIcu7EZMgzAe/HQZpz48lb9NXMrmffV3Fr6npqzi61W7uf+CrnRMjvU6HOPDq8F4jwDvish1OAn9UgARyQBuVtXrgfeBocAinIF5n6vqJx7Fa4wxP4mPDufKk1tx5cmtWLzlAC99vZZx363nte/Wc3a3ZG4e3I5uqfFehxkw01fu4qmpq7ioTypXnJTmdTimDE8SvaruAYb5eX82zuA7VLUYuCnAoRljzHHplhrPE5f35ndndeK179bz9syNTFy4jcEdmnLb0Pb0a53odYg1asv+PO4aP4+OSbE8NLJ7lcYsmMCyKaGMMaYaNE+I4g/ndOa2oe357/cbeOWbdVz6/Pec1CaRQY2LGKwadEmwoKiEW9+cS2Gx8uyVfWzCoVrK5ro3xphqFBcZzq1D2vPNPUP5y3ld2LjnEP+afZgRz3zL54u3U1ISHJN7Hi4q5p4PFjJ/034evaQHbZvWr9vZ6hJL9MYYUwOiIkK5dkAbpv8uk192jeBAXiE3vzGHs56cwf/mbaGouMTrEE/YtgN5jHrhBz6at4W7T+/AOd1TvA7JVMASvTHG1KAGYaEMTgtnyt2DefLyXgDc9c58hvw7i9e+XcehgiKPIzw+363ZzXlPfcPqHTk8d2Uf7hhmt9LVdpbojTEmAMJCQxjRK5XP7xzEC1f3pWlMA+7/ZCmnPjKVx75cwe7cw16HWCFV5cUZa7n65R9JiA7n49tO42xrydcJNhjPGGMCKCREOLNrMmd2TWb2+r28MGMtT09bzfMz1nJ+j+Zc1b8lvdISatXAvZU7cvjTR4v5cf1ezu6WzD8v7UlMA0sfdYX9SxljjEcyWieS0TqRNbtyefXbdXw0dwsfzN1Mt9Q4rjq5FRf0ak50hHd/pg8eLuKpKat4+Zt1xESG8fBF3bm8X1qt+hJiKmeJ3hhjPNauaQwPjuzOvWd35qN5W3jzhw3c++Ei/jZxKed0T+Hivi04qXViwJ6gV1yiTFq0jYcnLWPrgXwuy0jjnrM7kdjQnkZXF1miN8aYWiKmQRhX92/FVSe3ZPaGfbw3exOTFm3nvTmbadEoiot6p3Juj+Z0SIqpkVZ1QVEJ/5u3heenr2Ht7oN0So7lqSt6kxHkk/4EO0v0xhhTy4gI/Von0q91Ig9c0I0vl27n/TmbeXraap6auprWjaOd6/zdkunVIuFnt/R35x7m4/lbeenrtWw7kE/X5nE884s+nNUtmdAA9SKYmmOJ3hhjarGoiFBG9EplRK9UdubkM3npDr5YsoOXv1nHCzPWktgwgj4tE+jdshF9WjaiZ1p8pdf1C4tLmLdxP9NX7mT6yl0s3pINwMltEnnk4h4MSm9i1+GDiCV6Y4ypI5rFRv70MJ0DeYVMW76Tb1bvZu7GfXy1zHnad4hA45gGJEZHkNjQ+WkQFsLugwXszjnMrtzD7D1YQHGJEhoi9G3ZiN+e2ZHMjk3p2rz+PIinPrFEb4wxdVB8VDgje6cysncqAPsOFjBv0z7mbzrAzux89h4sYN+hApZtz+ZwYQlNYiJIiY+ke2o8TWIj6NY8ntPSmxAXGe7xkZiaZoneGGOCQKOGEQztlMTQTkleh2JqGZsZzxhjjAliluiNMcaYIGaJ3hhjjAliluiNMcaYIGaJ3hhjjAliluiNMcaYIGaJ3hhjjAlioqpex1CtRGQXsKGaN9sE2F3N2zT+WV0HjtV14FhdB1Z9rO9WqtrU34qgS/Q1QURmq2qG13HUB1bXgWN1HThW14Fl9X0067o3xhhjgpglemOMMSaIWaKvmrFeB1CPWF0HjtV14FhdB5bVtw+7Rm+MMcYEMWvRG2OMMUHMEn0FROQsEVkhIqtF5F6v4wkmIpImItNEZJmILBGRO933E0Vksoiscn838jrWYCEioSIyT0QmusttRGSmW9fviEiE1zEGCxFJEJH3RWS5e46fYud2zRCR/3P/hiwWkbdFJNLO7aNZoi+HiIQCzwBnA12AK0Ski7dRBZUi4Neq2hnoD9zq1u+9wBRVTQemuMumetwJLPNZ/gfwuFvX+4DrPIkqOD0JfK6qnYCeOPVu53Y1E5FU4A4gQ1W7AaHA5di5fRRL9OU7CVitqmtVtQAYD4zwOKagoarbVHWu+zoH5w9hKk4dj3OLjQNGehNhcBGRFsC5wEvusgBDgffdIlbX1URE4oBBwMsAqlqgqvuxc7umhAFRIhIGRAPbsHP7KJboy5cKbPJZ3uy+Z6qZiLQGegMzgSRV3QbOlwGgmXeRBZUngN8BJe5yY2C/qha5y3Z+V5+2wC7gVfdSyUsi0hA7t6udqm4B/gVsxEnwB4A52Ll9FEv05RM/79ktCtVMRGKAD4C7VDXb63iCkYicB+xU1Tm+b/spaud39QgD+gDPqWpv4CDWTV8j3HEOI4A2QHOgIc7l1rLq9bltib58m4E0n+UWwFaPYglKIhKOk+TfVNUP3bd3iEiKuz4F2OlVfEHkNOACEVmPcwlqKE4LP8Ht7gQ7v6vTZmCzqs50l9/HSfx2ble/4cA6Vd2lqoXAh8Cp2Ll9FEv05ZsFpLujNyNwBnhM8DimoOFeI34ZWKaqj/msmgCMdl+PBj4OdGzBRlV/r6otVLU1znk8VVWvBKYBl7jFrK6riapuBzaJSEf3rWHAUuzcrgkbgf4iEu3+TSmtazu3fdiEORUQkXNwWj6hwCuq+pDHIQUNERkAfA0s4sh14z/gXKd/F2iJ85/4UlXd60mQQUhEMoHfqOp5ItIWp4WfCMwDrlLVw17GFyxEpBfOwMcIYC3wS5yGlZ3b1UxEHgAuw7mTZx5wPc41eTu3XZbojTHGmCBmXffGGGNMELNEb4wxxgQxS/TGGGNMELNEb4wxxgQxS/TGGGNMELNEb4wxxgQxS/TG1CIi8t1xls8sfexsNey7t4i8VB3bKrPdMSLyH/f1zSJyTXXvowoxZIjIUyf42QgRmeEz05oxdYqduMbUIqp6qoe7/wPwYE3uQFWfr8ntV7Df2cDsE/xsgYhMwZmU5c1qDcyYALAWvTEBICL9RGShiESKSEMRWSIi3fyUy3V/Z4pIloi8LyLLReRNd4pPROQs971vgIt8PttQRF4RkVnuU9NGuO/fLSKvuK+7i8hiEYkus99YoIeqLnCX73e3lSUia0XkDp+yd7vbWCwid5VzvL8UkZUiMh1nrn18tvsb93WWiDzutpaXuXX0oYisEpEHfT5zlYj8KCLzReQFEQktrSsReUhEFojIDyKS5L5/qRvbAhGZ4VOfE93XiSLyP/ff4wcR6VHZMQP/A66s8B/ZmFrKEr0xAaCqs3DmOn8QeBR4Q1UXV/Kx3sBdQBecR5+eJiKRwIvA+cBAINmn/B9x5rHvBwwB/uk+HvUJoL2IXAi8CtykqofK7CsDKBtPJ+BM4CTgPhEJF5G+ONO5ngz0B24Qkd6+H3If2PIAToI/3Y2/PAWqOgh4Hmc+8luBbsAYEWksIp1xWtKnqWovoJgjCbch8IOq9gRmADe47/8FONN9/wI/+3wAmKeqPXB6MV6v6Jjd9xcD/So4DmNqLUv0xgTOX3ESXwZOsq/Mj6q6WVVLgPlAa5xEtE5VV6kzf/UbPuXPAO4VkflAFhAJtHQ/Pwb4LzBdVb/1s68UnGeo+/pUVQ+r6m6cJ60lAQOAj1T1oKrm4jwtbGCZz50MZLlPFCsA3qngGEsfFLUIWKKq29w5ydfiPD1yGNAXmOUe1zCcLz0ABUDp+IQ5bv0AfAu8JiI34DynoqwBOHWBqk4FGotIfAXHjKoWAwVuz4cxdYpdozcmcBKBGCAcJwkfrKS870M4ijny/7W8B1QIcLGqrvCzLh3IxXlmtz95bkyV7d/fc+z9qepDNEr3UVJmfyU++xunqr/389lCPfKwjp/qR1VvFpGTgXOB+e4DZnz5O4bS7ZRX5wANgPyKD8eY2sda9MYEzljgzzgDuv5xgttYDrQRkXbu8hU+674Abve5lt/b/R0PPAkMwmm9XsKxlgHtq7D/GcBI97GgDYELcZ5C6GsmkOl2vYcDl1bt0PyaAlwiIs3gp+vrrSr6gIi0U9WZqvoXYDdOz0DZY7jSLZsJ7FbV7Eq22Rgofea5MXWKteiNCQD3lrIiVX3LHUz2nYgMdbuOq0xV80XkRuBTEdkNfINzTRvgbzjX4xe6yX49cB7wOPCsqq4UkeuAaSIyQ1V3+mx3uYjEi0isquZUsP+5IvIa8KP71kuqOq9MmW0icj/wPbANmIv/LvSqHO9SEfkT8KWIhACFONfxN1TwsX+KSDpOy30KsAAY7LP+fuBVEVkIHOLIM+IrMgSYdPxHYIz37DG1xhgAROT/gBxVrfZ76es6EfkQ+H05l0WMqdWs694YU+o5jr5GbXAmzAH+Z0ne1FXWojfGGGOCmLXojTHGmCBmid4YY4wJYpbojTHGmCBmid4YY4wJYpbojTHGmCD2/2eltA2sFGIUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 3.5))\n",
    "\n",
    "plt.plot(ssh_time_0_tile_2[:,0])\n",
    "plt.title('<SSH> Jan2010 along x=0 for tile 2')\n",
    "plt.ylabel('SSH [m]');plt.xlabel('x index (no dimension)');\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that around y=80, SSH=0 because from around y=80 on we are on Greenland.  \n",
    "\n",
    "A more interesting plot might be to have the model latitude on the **x** axis instead of x-index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 3.5))\n",
    "\n",
    "yc_tile_2 = ecco_ds.YC.values[2,:,:]\n",
    "\n",
    "plt.plot(yc_tile_2[:,0],ssh_time_0_tile_2[:,0], color='k')\n",
    "plt.title('<SSH> Jan 2010 along x=0 for tile 2')\n",
    "plt.ylabel('SSH [m]');plt.xlabel('degrees N. latitude');\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can always use **[ ]** method to subset our `numpy` arrays.  It is a simple, direct method for accessing our fields.  Just for fun let's plot this SSH subset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting `DataArrays` using the **[ ]** syntax\n",
    "\n",
    "An interesting and useful alternative to subsetting `numpy` arrays with the **[ ]** method is to subset `DataArray` instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SSH' (j: 90, i: 90)>\n",
       "array([[0.10849833, 0.10763063, 0.10525029, ..., 0.        , 0.        ,\n",
       "        0.41118386],\n",
       "       [0.10236199, 0.1005336 , 0.09722137, ..., 0.31839028, 0.        ,\n",
       "        0.40181533],\n",
       "       [0.10205435, 0.09833906, 0.09325342, ..., 0.353757  , 0.35896647,\n",
       "        0.40689626],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "    tile     int64 2\n",
       "    XC       (j, i) float32 -37.5 -36.5 -35.5 ... 50.968143 51.44421 51.837925\n",
       "    YC       (j, i) float32 10.458642 10.458642 10.458642 ... 67.53387 67.47211\n",
       "    CS       (j, i) float32 1.0 1.0 1.0 1.0 ... 0.8916124 0.9051672 0.9424238\n",
       "    SN       (j, i) float32 -1.8064405e-15 9.046443e-16 ... -0.33442083\n",
       "    rA       (j, i) float32 11896091000.0 11896091000.0 ... 212633870.0\n",
       "    Depth    (j, i) float32 4658.681 4820.5703 5014.177 ... 0.0 0.0 0.0\n",
       "    iter     int32 158532\n",
       "    time     datetime64[ns] 2010-01-16T12:00:00\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Surface Height Anomaly adjusted with global steric height...\n",
       "    standard_name:  sea_surface_height"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_time_0_tile_2_DA = ecco_ds.SSH[0,2,:,:]\n",
    "ssh_time_0_tile_2_DA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting `DataArray` is a subset of the original `DataArray`.  The subset has two fewer dimensions (**tile** and **time** have been eliminated). The horizontal dimensions **j** and **i** are unchanged.\n",
    "\n",
    "Even though the **tile** and **time** dimensions have been eliminated, the dimensional and non-dimensional coordinates associated with **time** and **tile** remain.  In fact, these coordinates *tell us when in time and which tile our subset comes from*:\n",
    "\n",
    "```\n",
    "Coordinates:\n",
    "  * j        (j) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
    "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
    "    tile     int64 2\n",
    "    XC       (j, i) float32 -37.5 -36.5 -35.5 ... 50.968143 51.44421 51.837925\n",
    "    YC       (j, i) float32 10.458642 10.458642 10.458642 ... 67.53387 67.47211\n",
    "    Depth    (j, i) float32 4658.681 4820.5703 5014.177 ... 0.0 0.0 0.0\n",
    "    rA       (j, i) float32 11896091000.0 11896091000.0 ... 212633870.0\n",
    "    iter     int32 158532\n",
    "    time     datetime64[ns] 2010-01-16T12:00:00\n",
    "```\n",
    "\n",
    "Notice that the *tile* coordinate is 2 and the *time* coordinate is Jan 16, 2010, the middle of Jan.\n",
    "\n",
    "As a `DataArray` we can take full advantage of the built-in plotting functionality of ``xarray``.  This functionality, which we've seen a few times, automatically labels the figure (although sometimes the labels can be a little odd)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f71add1cf60>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "ssh_time_0_tile_2_DA.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting `DataArrays` using the **.sel( )** syntax\n",
    "\n",
    "Another useful method for subsetting `DataArrays` is the **.sel( )** syntax.  The **.sel( )** syntax takes advantage of the fact that coordinates are **labels**.  We **sel**ect subsets of the `DataArray` by providing a subset of coordinate labels.\n",
    "\n",
    "Let's select tile 2 and time 2010-01-16:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SSH' (time: 1, j: 90, i: 90)>\n",
       "array([[[0.10849833, 0.10763063, 0.10525029, ..., 0.        ,\n",
       "         0.        , 0.41118386],\n",
       "        [0.10236199, 0.1005336 , 0.09722137, ..., 0.31839028,\n",
       "         0.        , 0.40181533],\n",
       "        [0.10205435, 0.09833906, 0.09325342, ..., 0.353757  ,\n",
       "         0.35896647, 0.40689626],\n",
       "        ...,\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "    tile     int64 2\n",
       "    XC       (j, i) float32 -37.5 -36.5 -35.5 ... 50.968143 51.44421 51.837925\n",
       "    YC       (j, i) float32 10.458642 10.458642 10.458642 ... 67.53387 67.47211\n",
       "    CS       (j, i) float32 1.0 1.0 1.0 1.0 ... 0.8916124 0.9051672 0.9424238\n",
       "    SN       (j, i) float32 -1.8064405e-15 9.046443e-16 ... -0.33442083\n",
       "    rA       (j, i) float32 11896091000.0 11896091000.0 ... 212633870.0\n",
       "    Depth    (j, i) float32 4658.681 4820.5703 5014.177 ... 0.0 0.0 0.0\n",
       "    iter     (time) int32 158532\n",
       "  * time     (time) datetime64[ns] 2010-01-16T12:00:00\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Surface Height Anomaly adjusted with global steric height...\n",
       "    standard_name:  sea_surface_height"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_time_0_tile_2_sel = ecco_ds.SSH.sel(time='2010-01-16', tile=2)\n",
    "ssh_time_0_tile_2_sel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference here is that the resulting array has 'time' as a singleton dimension (dimension of length 1).  I don't know why.  Just use the ``squeeze`` command to squeeze it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SSH' (j: 90, i: 90)>\n",
       "array([[0.10849833, 0.10763063, 0.10525029, ..., 0.        , 0.        ,\n",
       "        0.41118386],\n",
       "       [0.10236199, 0.1005336 , 0.09722137, ..., 0.31839028, 0.        ,\n",
       "        0.40181533],\n",
       "       [0.10205435, 0.09833906, 0.09325342, ..., 0.353757  , 0.35896647,\n",
       "        0.40689626],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "    tile     int64 2\n",
       "    XC       (j, i) float32 -37.5 -36.5 -35.5 ... 50.968143 51.44421 51.837925\n",
       "    YC       (j, i) float32 10.458642 10.458642 10.458642 ... 67.53387 67.47211\n",
       "    CS       (j, i) float32 1.0 1.0 1.0 1.0 ... 0.8916124 0.9051672 0.9424238\n",
       "    SN       (j, i) float32 -1.8064405e-15 9.046443e-16 ... -0.33442083\n",
       "    rA       (j, i) float32 11896091000.0 11896091000.0 ... 212633870.0\n",
       "    Depth    (j, i) float32 4658.681 4820.5703 5014.177 ... 0.0 0.0 0.0\n",
       "    iter     int32 158532\n",
       "    time     datetime64[ns] 2010-01-16T12:00:00\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Surface Height Anomaly adjusted with global steric height...\n",
       "    standard_name:  sea_surface_height"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_time_0_tile_2_sel = ssh_time_0_tile_2_sel.squeeze()\n",
    "ssh_time_0_tile_2_sel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting `DataArrays` using the **.isel( )** syntax\n",
    "\n",
    "The last subsetting method is **.isel( )** syntax.  **.isel( )** uses the numerical **index** of coordinates instead of their label.  Subsets are extracted by providing a set of coordinate indices.\n",
    "\n",
    "Because **sel()** uses the coordinate values as LABELS and **isel()** uses the index of the coordinates, they cannot  necessarily be used interchangably.  It is only because in ECCOv4 NetCDF files we gave the NAMES of some coordinates the same names as the INDICES of those coordinates that you can use:\n",
    "* **sel(tile=2)** gives you the tile with the index NAME 2\n",
    "* **isel(tile=2)** gives you the tile at index 2\n",
    "\n",
    "Let's pull out a slice of *SSH* for tile at INDEX POSITION 2 and *time* at INDEX POSITION 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SSH' (j: 90, i: 90)>\n",
       "array([[0.10849833, 0.10763063, 0.10525029, ..., 0.        , 0.        ,\n",
       "        0.41118386],\n",
       "       [0.10236199, 0.1005336 , 0.09722137, ..., 0.31839028, 0.        ,\n",
       "        0.40181533],\n",
       "       [0.10205435, 0.09833906, 0.09325342, ..., 0.353757  , 0.35896647,\n",
       "        0.40689626],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * i        (i) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int64 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "    tile     int64 2\n",
       "    XC       (j, i) float32 -37.5 -36.5 -35.5 ... 50.968143 51.44421 51.837925\n",
       "    YC       (j, i) float32 10.458642 10.458642 10.458642 ... 67.53387 67.47211\n",
       "    CS       (j, i) float32 1.0 1.0 1.0 1.0 ... 0.8916124 0.9051672 0.9424238\n",
       "    SN       (j, i) float32 -1.8064405e-15 9.046443e-16 ... -0.33442083\n",
       "    rA       (j, i) float32 11896091000.0 11896091000.0 ... 212633870.0\n",
       "    Depth    (j, i) float32 4658.681 4820.5703 5014.177 ... 0.0 0.0 0.0\n",
       "    iter     int32 158532\n",
       "    time     datetime64[ns] 2010-01-16T12:00:00\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Surface Height Anomaly adjusted with global steric height...\n",
       "    standard_name:  sea_surface_height"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_time_0_tile_2_isel = ecco_ds.SSH.isel(tile=2, time=0)\n",
    "ssh_time_0_tile_2_isel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More examples of subsetting using the **[ ]**, **.sel( )** and **.isel( )** syntaxes\n",
    "\n",
    "In the examples above we only subsetted month (Jan 2010) and a single tile (tile 2).  More complex subsetting is possible.  Here are some three examples that yield equivalent, more complex, subsets:\n",
    "\n",
    "  > **Note:** Python array indexing goes up to but not including the final number in a range.  Because array indexing starts from 0, array index 41 corresponds to the 42nd element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ssh_sub_bracket\n",
      "--size (10, 17) \n",
      "--time 2010-04-16T12:00:00.000000000 \n",
      "--tile 5 \n",
      "\n",
      "ssh_sub_isel\n",
      "--size (10, 17) \n",
      "--time 2010-04-16T12:00:00.000000000 \n",
      "--tile 5 \n",
      "\n",
      "ssh_sub_isel\n",
      "--size (10, 17) \n",
      "--time 2010-03-16T12:00:00.000000000 \n",
      "--tile 5 \n"
     ]
    }
   ],
   "source": [
    "ssh_sub_bracket  = ecco_ds.SSH[3, 5, 31:41, 5:22]\n",
    "ssh_sub_isel     = ecco_ds.SSH.isel(tile=5, time=3, i=range(5,22), j=range(31,41))\n",
    "ssh_sub_sel      = ecco_ds.SSH.sel(tile=5, time='2010-03-16', i=range(5,22), j=range(31,41)).squeeze()\n",
    "\n",
    "print('\\nssh_sub_bracket')\n",
    "print('--size %s ' % str(ssh_sub_bracket.shape))\n",
    "print('--time %s ' % ssh_sub_bracket.time.values)\n",
    "print('--tile %s ' % ssh_sub_bracket.tile.values)\n",
    "\n",
    "print('\\nssh_sub_isel')\n",
    "print('--size %s ' % str(ssh_sub_isel.shape))\n",
    "print('--time %s ' % ssh_sub_isel.time.values)\n",
    "print('--tile %s ' % ssh_sub_isel.tile.values)\n",
    "\n",
    "\n",
    "print('\\nssh_sub_isel')\n",
    "print('--size %s ' % str(ssh_sub_sel.shape))\n",
    "print('--time %s ' % ssh_sub_sel.time.values)\n",
    "print('--tile %s ' % ssh_sub_sel.tile.values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting `Datasets` using the **.sel( )**, and **.isel( )** syntaxes\n",
    "\n",
    "Amazingly, we can use the **.sel** and **.isel** methods to simultaneously subset multiple `DataArrays` stored within an single `Dataset`.  Let's make an interesting `Dataset` to subset and then test out the **.sel( )** and **.isel( )** subsetting methods.\n",
    "\n",
    "Let's work on tile 1, time = 5 (June, 2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f71adc03a90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(8, 6.5))\n",
    "\n",
    "ecco_ds.SSH.isel(tile=1,time=5).plot(cmap='jet',vmin=-2, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset tile 1, j = 50 (a single row through the array), and time = 5 (June 2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    SSH      (i) float32 0.2910574 0.30933306 0.3110007 ... 0.8554105 0.8880241\n",
       "    OBP      (i) float32 197.39972 265.14847 298.19925 ... 365.57407 446.80014"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_tile1_time06_j50= ecco_ds.isel(tile=1, time=5, j=50).load()\n",
    "output_tile1_time06_j50.data_vars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All variables that had **tile, time**, or **j** coordinates have been subset while other variables are unchanged.  Let's plot the seafloor depth and sea surface height from west to east along j=50, (see plot at Line 16) which extends across the S. Atlantic, across Africa to Madagascar, and finally into to W. Indian Ocean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, sharex=True,figsize=(8, 8))\n",
    "(ax1, ax2) = axarr\n",
    "ax1.plot(output_tile1_time06_j50.XC, output_tile1_time06_j50.SSH,color='b')\n",
    "ax1.set_ylabel('m')\n",
    "ax1.set_title('SSH (m)')\n",
    "ax1.grid()\n",
    "\n",
    "ax2.plot(output_tile1_time06_j50.XC, -output_tile1_time06_j50.Depth,color='k')\n",
    "ax2.set_xlabel('longitude')\n",
    "ax2.set_ylabel('m')\n",
    "ax2.set_title('Seafloor Depth (m)')\n",
    "ax2.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting using the **where( )** syntax\n",
    "\n",
    "The **where( )** method is quite different than other subsetting methods because  subsetting is done by masking out values with *nans* that do not meet some specified criteria.  \n",
    "\n",
    "For more infomation about **where( )** see http://xarray.pydata.org/en/stable/indexing.html#masking-with-where\n",
    "\n",
    "Let's demonstrate **where** by masking out all SSH values that do not fall within a box defined between 20S to 60N and 50W to 10E.\n",
    "\n",
    "First, we'll extract the ``SSH`` `DataArray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssh_da=ecco_ds.SSH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a matrix that is `True` where latitude is between 20S and 60N and `False` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_bounds = np.logical_and(ssh_da.YC  > -20, ssh_da.YC < 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a matrix that is `True` where longitude is between 50W and 10E and `False` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon_bounds = np.logical_and(ssh_da.XC  > -50, ssh_da.XC < 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the ``lat_bounds`` and ``lon_bounds`` logical matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_lon_bounds = np.logical_and(lat_bounds, lon_bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use **where** to mask out all SSH values that do not fall within our  ``lat_lon_bounds``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssh_da_subset_space = ssh_da.where(lat_lon_bounds, np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the SSH in our box we'll use one of our ECCO v4 custom plotting routines (which will be the subject of another tutorial).  \n",
    "\n",
    "Notice the use of **.sel( )** to subset a single time slice (time=1) for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(14, 6))\n",
    "\n",
    "ecco.plot_proj_to_latlon_grid(ecco_ds.XC, ecco_ds.YC, \\\n",
    "                              ssh_da_subset_space.isel(time=6),\\\n",
    "                              dx=.5, dy=.5,user_lon_0 = -30,\\\n",
    "                              show_colorbar=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "You now know several different methods for accessing and subsetting fields in `Dataset` and `DataArray` objects.    \n",
    "\n",
    "To learn a more about indexing/subsetting methods please refer to the `xarray` manual for indexing methods, http://xarray.pydata.org/en/stable/indexing.html.  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
